Informe 2 Series de Tiempo y Redes Sociales NVIDIA
Ramon Ariel Mosquer García - Marlon Martínez Campo
21/11/2020
require(tseries)
require(forecast)
require(xts)
require(fGarch)
require(zoo)
con=url("https://finance.yahoo.com/")
nvidia = get.hist.quote(instrument = "NVDA", start = "2016-11-01",quote = "Close")## time series ends 2020-11-20plot(nvidia)
Entre el 2016 y 2020 la acción de NVIDIA ha mantenido una tendencia alcista, pasado de US$92,22 a US$537,15 dólares por acción.
nvidia_month=to.monthly(nvidia)
nvidia_month## nvidia.Open nvidia.High nvidia.Low nvidia.Close
## nov. 2016 69.05 94.16 67.57 92.20
## dic. 2016 87.64 117.32 87.64 106.74
## ene. 2017 102.01 111.77 101.11 109.18
## feb. 2017 113.95 119.13 100.49 101.48
## mar. 2017 102.79 109.45 97.67 108.93
## abr. 2017 108.38 108.38 95.49 104.30
## may. 2017 106.64 144.87 102.77 144.35
## jun. 2017 144.36 159.94 143.64 144.56
## jul. 2017 139.33 168.10 139.33 162.51
## ago. 2017 164.49 172.35 155.96 169.44
## sept. 2017 170.46 187.55 163.69 178.77
## oct. 2017 179.00 206.81 179.00 206.81
## nov. 2017 207.20 216.96 196.42 200.71
## dic. 2017 197.68 197.90 186.18 193.50
## ene. 2018 199.35 246.85 199.35 245.80
## feb. 2018 240.50 249.08 213.70 242.00
## mar. 2018 232.21 250.48 221.35 231.59
## abr. 2018 221.05 237.54 214.25 224.90
## may. 2018 227.14 260.13 226.31 252.19
## jun. 2018 257.62 266.91 235.72 236.90
## jul. 2018 242.24 254.84 236.84 244.86
## ago. 2018 246.47 280.68 244.82 280.68
## sept. 2018 283.70 283.70 263.45 281.02
## oct. 2018 289.36 289.36 185.62 210.83
## nov. 2018 218.11 218.11 144.70 163.43
## dic. 2018 170.04 170.04 127.08 133.50
## ene. 2019 136.22 160.15 127.99 143.75
## feb. 2019 144.73 159.19 144.73 154.26
## mar. 2019 156.45 183.94 149.26 179.56
## abr. 2019 182.28 192.10 178.09 181.00
## may. 2019 180.47 183.19 135.46 135.46
## jun. 2019 133.78 164.23 133.78 164.23
## jul. 2019 166.17 178.66 157.21 168.72
## ago. 2019 164.92 171.48 148.77 167.51
## sept. 2019 164.17 184.33 164.17 174.07
## oct. 2019 174.00 206.79 173.04 201.02
## nov. 2019 202.59 221.21 202.59 216.74
## dic. 2019 209.25 239.37 207.66 235.30
## ene. 2020 239.91 252.86 236.07 236.43
## feb. 2020 240.33 314.70 240.33 270.07
## mar. 2020 276.43 284.51 196.40 263.60
## abr. 2020 243.07 298.46 243.07 292.28
## may. 2020 282.78 361.05 282.78 355.02
## jun. 2020 352.25 381.07 350.66 379.91
## jul. 2020 381.20 424.59 381.20 424.59
## ago. 2020 440.41 534.98 434.00 534.98
## sept. 2020 552.84 573.86 476.52 541.22
## oct. 2020 544.58 569.93 501.36 501.36
## nov. 2020 503.23 582.48 503.23 523.51plot(nvidia_month$nvidia.Open)
nvidia_month_decom=decompose(nvidia_month$nvidia.Open)par(mfrow=c(4,1))
plot(nvidia_month$nvidia.Open,main="Serie de Nvidia")
plot(nvidia_month_decom$trend,main="Tendencia")
plot(nvidia_month_decom$seasonal,main="Estacionalidad")
barplot(nvidia_month_decom$seasonal[1:12],names.arg = month.name,main="Efecto Estacionalidad")
mod_nvidia=auto.arima(nvidia_month$nvidia.Open)
mod_nvidia## Series: nvidia_month$nvidia.Open
## ARIMA(0,1,1)(1,0,0)[12] with drift
##
## Coefficients:
## ma1 sar1 drift
## 0.2348 -0.3324 7.6111
## s.e. 0.1369 0.1643 4.1318
##
## sigma^2 estimated as 883.6: log likelihood=-230.11
## AIC=468.21 AICc=469.14 BIC=475.7forecast(mod_nvidia,h=6)## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Dec 2020 503.3216 465.2275 541.4156 445.0617 561.5814
## Jan 2021 503.2705 442.7416 563.7995 410.6995 595.8416
## Feb 2021 513.2722 436.6149 589.9296 396.0349 630.5096
## Mar 2021 511.4128 421.4742 601.3514 373.8636 648.9620
## Apr 2021 532.6441 431.1475 634.1406 377.4185 687.8696
## May 2021 529.5845 417.7179 641.4511 358.4993 700.6698par(mfrow=c(1,1))
plot(forecast(mod_nvidia,h=6))
Tendencia y Estacionalidad Se puede concluir que la acción registra altas valoraciones en los meses de noviembre cuando se dan a conocer los resultados corporativos del tercer trimestre.
y presenta desvaloraciones en los meses de diciembre y marzo. En noviembre de 2016 las acciones subieron un 30% por los buenos resultados obtenidos en el 3Q. La Corporación realiza el cierre contable de año los 31 de enero.
Modelo ARIMA Los pronósticos son lógicos en comparación con la serie, teniendo en cuenta que la acción presenta valorizaciones en enero, agosto, septiembre, octubre, noviembre y diciembre. #Las desvaloraciones se presentan entre febrero y julio.
REDES SOCIALES - @NVIDIAGeForceLA
Twitter de NVIDIA GeForce LA
La siguiente información corresponde a los primeros 10 trinos de NVIDIA GeForce LA
library(tidyverse)
library(tidytext)
library(tm)
library(lubridate)
library(zoo)
library(scales)
require(ggplot2)
library(rtweet)
require(quanteda)
library(readr)
library(RColorBrewer)
library(dplyr)
library(janeaustenr)
categorias_afinn <- read_csv("MF - JAVERIANA 2019/20. ECONOMETRIA FINANCIERA/categorias_afinn.csv")
appname = "consuldesarrollo"
key = "HFUMJb9cFCa8g4DD52JuVHOeX"
secret = "3VsKccqPO8GVnjlRWrQc156fzYetE9PF3RIbzD1drlbzBdUQg3"
access_token = "1318649634361921537-ae9vZ3XFlPxEpETVl7mmLpPXiUzpWk"
access_secret = "Bg0Tb2wHbedeDX3HVxJaTovFj5xGIXLKKpH2tm8TXVJyV"
token2="AAAAAAAAAAAAAAAAAAAAAK8gJAEAAAAAxtKf0iskpr1OneA47C249NLJ9Vw%3D9sXysfJLbu2zjfob00SkaGDVFJ6QSoPwXlynSPrtbn4EaJ7ki3"
twitter_token <- create_token(
app = appname,
consumer_key = key,
consumer_secret = secret,
access_token = access_token,
access_secret = access_secret
)
tweets_nvidia <- get_timelines("@NVIDIAGeForceLA",
n = 50000,
language = 'es',
since = '2019-01-01',
until = '2020-11-21',
token = twitter_token)
tweets_nvidia$text[1:10]## [1] "<U+0001F49A> @dedreviil te deseamos que pases un día muy genial ¡Feliz Cumpleaños! <U+0001F388><U+0001F38A><U+0001F389><U+0001F382><U+0001F973> https://t.co/hFPaoL7AKu"
## [2] "@maurobbto @michaelpcmaster Tenemos muchos monitores con tecnología G-Sync y G-Sync compatibles aquí hay mira https://t.co/D20z0cSF3L"
## [3] "<U+0001F333> HOY <U+0001F333> @michaelpcmaster estará en vivo mostrando el poder de #RTXOn. NVIDIA Reflex y DLSS en #BlackOpsColdWar y regalará una copia del juego también <U+0001F381> las 7pm MX, en https://t.co/CaO5UdGdor https://t.co/KBc42NXiE1"
## [4] "@DasMetalGabs @grako999 Le sobran manos a @DasMetalGabs es como pulpo jajajaja <U+0001F49A>"
## [5] "Hay muchísimos para elegir con tantos lanzamientos. <U+0001F601> #NVIDIAPregunta https://t.co/vxuGEomDpg"
## [6] "Experimenta #FortniteRTX bajo una nueva luz con las sombras #RTXOn de NVIDIA, la iluminación global y la oclusión ambiental. <U+0001F64C>\n\nGameplay por: @Jack1_1Hammer https://t.co/PVgcrhg0Zd"
## [7] "¡Nuestro concurso #GiftAGamer para las fiestas 2020 ya está aquí! <U+0001F49A> <U+2B50> <U+0001F381> \n\nAsí compartes el amor (¡y ganas!):\n1. RT esta foto.\n2. Di algo agradable de tu amigo gamer y etiquétalo en las respuestas con #GiftAGamer\n3. ¡Si te seleccionan tu + el amigo AMBOS ganará un RTX 3080! https://t.co/lvKew5vKaN"
## [8] "Prepárate para sobrevivir en Night City y disfrutar de la mejor experiencia con las specs de PC oficiales para #RTXOn\n\nMás info <U+27A1><U+FE0F> https://t.co/KN5hSiyx0S https://t.co/AzZblqFQSr"
## [9] "\"La primera vez que lo vi todo...me estalló la cabeza.\" <U+0001F92F>\n\nTe mostramos unas tomas exclusivas de #Cyberpunk2077 con #RTXOn de la mano de los desarrolladores de @CDPROJEKTRED.\n\nTodo el gameplay #RTXOn ha sido capturado con RTX Serie 30. Más info <U+27A1><U+FE0F> https://t.co/KN5hSiyx0S https://t.co/EwTkfYD8DX"
## [10] "Ya estámos en vivo #BlackOpsColdWar https://t.co/wqgpBYkJFr https://t.co/ceUe5leItO"##Selección de Tweets
tweets_nvidia <- tweets_nvidia[tweets_nvidia$is_retweet == F,]
tweets_nvidia_clientes=tweets_nvidia[!is.na(tweets_nvidia$reply_to_screen_name),]
## Paso 1 - ahora creo el CORPUS
mycorpus_nvidia <- corpus(tweets_nvidia$text)
mycorpus_nvidia[10]## Corpus consisting of 1 document.
## text10 :
## "Ya estámos en vivo #BlackOpsColdWar https://t.co/wqgpBYkJFr ..."head(summary(mycorpus_nvidia))## Text Types Tokens Sentences
## 1 text1 19 19 2
## 2 text2 14 15 1
## 3 text3 30 34 1
## 4 text4 11 12 1
## 5 text5 11 11 2
## 6 text6 26 27 2##Descomposición de palabras
## Paso 2 - TOKENIZATION (separar las palabras)
mycorpus_nvidia.wd <- tokens(mycorpus_nvidia, what = "word")
mycorpus_nvidia.wd[10]## Tokens consisting of 1 document.
## text10 :
## [1] "Ya" "estámos"
## [3] "en" "vivo"
## [5] "#BlackOpsColdWar" "https://t.co/wqgpBYkJFr"
## [7] "https://t.co/ceUe5leItO"##depurar Texto
## Paso 3 NORMALIZACION DE TEXTOS
mycorpus_nvidia.wd_numb_punct_symb <- tokens(mycorpus_nvidia, what = "word", remove_url = T, remove_numbers = T,
remove_punct = T, remove_symbols = T, remove_separators = T,
remove_twitter = T)
mycorpus_nvidia.wd_numb_punct_symb[10]## Tokens consisting of 1 document.
## text10 :
## [1] "Ya" "estámos" "en" "vivo"
## [5] "#BlackOpsColdWar"##stopwords
## Paso 4 - STOPWORDS
stopwords.es_nvidia <- stopwords('es')
stopwords.es_nvidia=c(stopwords.es_nvidia,"@NVIDIAGeForceLA","aquí","así","solo","tuit","sé","cómo","hoy","bro","the","ahí","creo","bob","esponja","pues","@everson_mayen","ahora","vas","tarde","@iliana7","@soy_pame","@enrique_talento","@flowerfigueroah","@andrea_mora31","@regiscorza","@javier34501576","re") ##para eliminar palabras
mycorpus_sw_nvidia <- tokens_remove(mycorpus_nvidia.wd_numb_punct_symb, stopwords.es_nvidia)
mycorpus_sw_nvidia[10]## Tokens consisting of 1 document.
## text10 :
## [1] "estámos" "vivo" "#BlackOpsColdWar"##STEMMING
## Paso 5 - STEMMING (opcional)
# encuentro las raices de las palabras
mycorpus_stem_nvidia<-tokens_wordstem(mycorpus_sw_nvidia, language = "es")##Creación del documento
## Paso 6 - CREACION DE LA DOCUMENT TERM MATRIX
dtm_sw_nvidia <- dfm(mycorpus_stem_nvidia) ##con steamming
dtm_sw_nvidia <- dfm(mycorpus_sw_nvidia) ##sin steamming
dtm_sw_nvidia## Document-feature matrix of: 2,869 documents, 6,829 features (99.9% sparse).
## features
## docs @dedreviil deseamos pases día genial feliz cumpleaños @maurobbto
## text1 1 1 1 1 1 1 1 0
## text2 0 0 0 0 0 0 0 1
## text3 0 0 0 0 0 0 0 0
## text4 0 0 0 0 0 0 0 0
## text5 0 0 0 0 0 0 0 0
## text6 0 0 0 0 0 0 0 0
## features
## docs @michaelpcmaster monitores
## text1 0 0
## text2 1 1
## text3 1 0
## text4 0 0
## text5 0 0
## text6 0 0
## [ reached max_ndoc ... 2,863 more documents, reached max_nfeat ... 6,819 more features ]#las palabras mas frecuentes
topfeatures(dtm_sw_nvidia, 20)## #rtxon rtx geforce juego
## 351 351 351 220
## si pc nvidia jugar
## 200 168 158 135
## mira #ultimatecountdown @mymtumtum @tortillasquad
## 133 124 119 112
## @ag_bean3r gtx juegos mejor
## 112 103 101 98
## nueva disponible of #nvidiapregunta
## 97 97 90 89##Remover texto con menos de 2 palabras y terminos con menos de 10
## Paso 7 - Remover documentos con menos de 2 (palabras) y terminos con menos de 10 (documentos)
dtm_f10_nvidia<-dfm_trim(dtm_sw_nvidia, min_termfreq = 10, min_docfreq = 2)
dtm_f10_nvidia## Document-feature matrix of: 2,869 documents, 427 features (99.0% sparse).
## features
## docs día genial feliz cumpleaños @michaelpcmaster monitores tecnología
## text1 1 1 1 1 0 0 0
## text2 0 0 0 0 1 1 1
## text3 0 0 0 0 1 0 0
## text4 0 0 0 0 0 0 0
## text5 0 0 0 0 0 0 0
## text6 0 0 0 0 0 0 0
## features
## docs g-sync mira vivo
## text1 0 0 0
## text2 2 1 0
## text3 0 0 1
## text4 0 0 0
## text5 0 0 0
## text6 0 0 0
## [ reached max_ndoc ... 2,863 more documents, reached max_nfeat ... 417 more features ]topfeatures(dtm_f10_nvidia,20)## #rtxon rtx geforce juego
## 351 351 351 220
## si pc nvidia jugar
## 200 168 158 135
## mira #ultimatecountdown @mymtumtum @tortillasquad
## 133 124 119 112
## @ag_bean3r gtx juegos mejor
## 112 103 101 98
## nueva disponible of #nvidiapregunta
## 97 97 90 89##Grafico de Nube
##Visualizacion WordCloud
library(RColorBrewer)
textplot_wordcloud(dtm_f10_nvidia, min_count = 20, random_order = FALSE,
rotation = .25, min_size = 1, max_size = 5,
color = RColorBrewer::brewer.pal(8,"Dark2"))
Las palabras más utilizadas por los usuarios en twitter es geforce, #rtxon, juego, nvidia, jugar, pc, emtre otros, correspondiente a las tarjetas gráficas de segunda generación, presentadas recientemente al público, permite tener un mejor desempeño en cuanto a potencia y calidad gráfica con los juegos más exigentes.
##Analisis de sentimientos
library(readr)
library(dplyr)
library(tidytext)
categorias_afinn <- read_csv("MF - JAVERIANA 2019/20. ECONOMETRIA FINANCIERA/categorias_afinn.csv")
tuits_afinn_nvidia <-
tweets_nvidia%>%
unnest_tokens(input = "text", output = "Palabra") %>%
inner_join(categorias_afinn, ., by = "Palabra") %>%
mutate(Tipo = ifelse(Puntuacion > 0, "Positiva", "Negativa"))
tuits_nvidia <-
tuits_afinn_nvidia %>%
group_by(status_id) %>%
summarise(Puntuacion_tuit = mean(Puntuacion)) %>%
left_join(tweets_nvidia, ., by = "status_id") %>%
mutate(Puntuacion_tuit = ifelse(is.na(Puntuacion_tuit), 0, Puntuacion_tuit))
median(tuits_nvidia$Puntuacion_tuit)## [1] 0hist(tuits_nvidia$Puntuacion_tuit,col="gray",main="Histograma Score de los Sentimientos Trinos de @NVIDIAGeForceLA")
tuits_nvidia$fecha=as.Date(tuits_nvidia$created_at)
tuits_nvidia$Puntuacion_tuit## [1] 3.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [7] 2.6666667 2.5000000 0.0000000 0.0000000 0.0000000 0.0000000
## [13] 2.3333333 2.3333333 0.0000000 0.0000000 0.0000000 0.5000000
## [19] 0.0000000 2.0000000 0.0000000 2.0000000 0.0000000 2.0000000
## [25] 3.0000000 -1.0000000 -1.0000000 0.0000000 0.0000000 0.0000000
## [31] 0.5000000 -1.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [37] 0.0000000 2.0000000 1.0000000 0.0000000 0.0000000 -1.0000000
## [43] 1.0000000 -0.7500000 3.3333333 3.0000000 -1.0000000 3.0000000
## [49] 2.6666667 1.0000000 1.5000000 0.0000000 0.0000000 0.0000000
## [55] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 -2.0000000
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## [199] 0.0000000 3.0000000 0.0000000 0.0000000 -1.5000000 0.0000000
## [205] 0.0000000 -1.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [211] 0.0000000 0.0000000 0.0000000 -1.0000000 2.3333333 2.0000000
## [217] 0.0000000 0.0000000 2.0000000 0.0000000 0.0000000 0.0000000
## [223] 0.0000000 3.0000000 0.0000000 1.0000000 0.0000000 0.0000000
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## [253] 0.0000000 -0.6000000 0.0000000 0.2000000 0.0000000 0.0000000
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## [283] 0.0000000 2.0000000 -1.0000000 -2.0000000 0.0000000 -0.3333333
## [289] -1.0000000 3.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [295] 0.3333333 0.0000000 -1.3333333 -1.0000000 -2.0000000 0.0000000
## [301] 3.0000000 2.0000000 1.0000000 4.0000000 1.0000000 -1.3333333
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## [403] 3.0000000 0.0000000 3.0000000 0.0000000 0.0000000 3.0000000
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## [1495] 0.0000000 0.0000000 0.0000000 -1.0000000 3.0000000 0.0000000
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## [1867] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.0000000
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## [1897] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 2.0000000
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## [2443] 0.0000000 -1.0000000 0.0000000 0.0000000 0.0000000 1.5000000
## [2449] 4.0000000 3.0000000 0.0000000 1.5000000 -1.0000000 0.0000000
## [2455] 3.0000000 0.0000000 0.0000000 0.0000000 3.0000000 -1.0000000
## [2461] 3.0000000 0.0000000 1.0000000 2.0000000 0.0000000 0.0000000
## [2467] 3.0000000 0.0000000 0.0000000 -1.0000000 0.0000000 0.0000000
## [2473] -1.0000000 0.0000000 0.0000000 0.0000000 0.0000000 3.0000000
## [2479] -1.0000000 0.0000000 0.0000000 0.0000000 2.0000000 0.0000000
## [2485] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2491] 0.0000000 -2.0000000 2.0000000 0.0000000 0.0000000 1.5000000
## [2497] 3.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2503] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2509] 0.0000000 0.0000000 -1.8750000 0.0000000 0.0000000 1.0000000
## [2515] 3.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2521] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2527] 0.0000000 0.0000000 3.0000000 2.0000000 3.0000000 0.0000000
## [2533] -2.0000000 0.0000000 0.0000000 0.0000000 1.0000000 -1.8750000
## [2539] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2545] 0.0000000 0.0000000 0.0000000 0.0000000 3.0000000 0.0000000
## [2551] 0.0000000 0.0000000 0.0000000 0.0000000 2.0000000 0.0000000
## [2557] -0.3333333 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2563] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2569] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2575] 1.0000000 0.0000000 -1.0000000 0.0000000 0.0000000 0.0000000
## [2581] 0.0000000 -1.0000000 -1.0000000 2.0000000 1.5000000 0.0000000
## [2587] 0.0000000 0.0000000 0.0000000 2.0000000 0.0000000 0.0000000
## [2593] 0.0000000 0.0000000 0.0000000 -1.0000000 1.5000000 0.0000000
## [2599] -1.0000000 2.0000000 3.0000000 3.0000000 0.0000000 0.0000000
## [2605] 0.0000000 3.0000000 0.0000000 -2.0000000 0.0000000 2.0000000
## [2611] 2.0000000 2.0000000 0.0000000 0.0000000 0.0000000 3.0000000
## [2617] 0.0000000 -1.0000000 2.0000000 0.0000000 0.0000000 0.0000000
## [2623] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2629] 0.0000000 -2.0000000 -2.0000000 0.0000000 0.0000000 2.0000000
## [2635] 0.0000000 1.0000000 0.0000000 0.0000000 -2.0000000 0.0000000
## [2641] -1.0000000 0.0000000 0.0000000 -2.0000000 -0.5000000 0.0000000
## [2647] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 -1.0000000
## [2653] 3.0000000 0.0000000 0.0000000 2.0000000 0.0000000 0.0000000
## [2659] -1.0000000 0.0000000 -2.0000000 2.0000000 -1.5000000 -1.0000000
## [2665] 2.0000000 0.3333333 0.0000000 0.0000000 0.0000000 3.5000000
## [2671] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2677] 0.0000000 2.0000000 2.0000000 2.0000000 1.0000000 0.0000000
## [2683] -1.3333333 1.0000000 0.0000000 0.0000000 0.0000000 0.5000000
## [2689] 3.0000000 3.0000000 0.0000000 -1.5000000 0.0000000 0.0000000
## [2695] 0.0000000 0.0000000 0.0000000 -1.0000000 -1.0000000 0.5000000
## [2701] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2707] 2.0000000 0.0000000 2.0000000 2.0000000 0.0000000 0.0000000
## [2713] -1.5000000 -0.2500000 2.0000000 2.0000000 0.0000000 0.0000000
## [2719] 1.0000000 3.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2725] 0.0000000 0.0000000 0.0000000 0.0000000 3.0000000 0.0000000
## [2731] 0.0000000 -0.5000000 0.0000000 -0.3333333 0.0000000 0.0000000
## [2737] -2.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2743] 0.0000000 2.0000000 -1.0000000 0.0000000 0.0000000 0.0000000
## [2749] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2755] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2761] 0.0000000 0.0000000 -1.0000000 -2.0000000 0.0000000 0.0000000
## [2767] -1.5000000 1.0000000 3.0000000 0.0000000 0.0000000 2.3333333
## [2773] 2.5000000 0.0000000 0.0000000 1.0000000 0.0000000 0.0000000
## [2779] 0.0000000 0.0000000 0.0000000 0.0000000 -1.0000000 0.0000000
## [2785] 0.0000000 0.0000000 2.0000000 -2.0000000 1.0000000 0.0000000
## [2791] 2.0000000 -2.0000000 2.0000000 0.0000000 0.0000000 0.0000000
## [2797] 0.0000000 2.0000000 3.0000000 -2.0000000 0.0000000 0.0000000
## [2803] 0.0000000 0.0000000 0.0000000 0.0000000 3.0000000 0.0000000
## [2809] 0.0000000 -2.0000000 1.0000000 0.0000000 -1.0000000 2.0000000
## [2815] 0.0000000 1.5000000 2.0000000 3.0000000 0.0000000 0.0000000
## [2821] 0.0000000 -5.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2827] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2833] 0.0000000 0.0000000 2.0000000 0.0000000 0.0000000 0.0000000
## [2839] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2845] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2851] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2857] 0.0000000 0.0000000 -1.0000000 0.0000000 0.0000000 0.0000000
## [2863] 0.0000000 -1.5000000 0.0000000 0.0000000 0.0000000 0.0000000
## [2869] 0.0000000ggplot(data=tuits_nvidia,aes(x=fecha,y=Puntuacion_tuit))+geom_smooth()+theme_bw()+geom_hline(yintercept = 0,col="red")+ggtitle("Serie de Tiempo del Score de Sentimiento para la Empresa @NVIDIAGeForceLA")
Se observa que las palaboras utilizadas por los usuarios tienen un impacto significativo, muchos de los comentarios contienen palabras que indican una buena aceptación de los productos creados por la empresa y lanzados al mercado con el propósito de mejorar el desempeño y calidad gráfica.